Method for Refining Cognitive Insights Using Cognitive Graph Vectors

ABSTRACT

A method, system and computer-usable medium for using cognitive graph vectors to refine cognitive insights.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method for usingcognitive graph vectors.

2. Description of the Related Art

In general, “big data” refers to a collection of datasets so large andcomplex that they become difficult to process using typical databasemanagement tools and traditional data processing approaches. Thesedatasets can originate from a wide variety of sources, includingcomputer systems, mobile devices, credit card transactions, televisionbroadcasts, and medical equipment, as well as infrastructures associatedwith cities, sensor-equipped buildings and factories, and transportationsystems. Challenges commonly associated with big data, which may be acombination of structured, unstructured, and semi-structured data,include its capture, curation, storage, search, sharing, analysis andvisualization. In combination, these challenges make it difficult toefficiently process large quantities of data within tolerable timeintervals.

Nonetheless, big data analytics hold the promise of extracting insightsby uncovering difficult-to-discover patterns and connections, as well asproviding assistance in making complex decisions by analyzing differentand potentially conflicting options. As such, individuals andorganizations alike can be provided new opportunities to innovate,compete, and capture value.

One aspect of big data is “dark data,” which generally refers to datathat is either not collected, neglected, or underutilized. Examples ofdata that is not currently being collected includes location data priorto the emergence of companies such as Foursquare or social data prior tothe advent companies such as Facebook. An example of data that is beingcollected, but is difficult to access at the right time and place,includes data associated with the side effects of certain spider biteswhile on a camping trip. As another example, data that is collected andavailable, but has not yet been productized of fully utilized, mayinclude disease insights from population-wide healthcare records andsocial media feeds. As a result, a case can be made that dark data mayin fact be of higher value than big data in general, especially as itcan likely provide actionable insights when it is combined withreadily-available data.

SUMMARY OF THE INVENTION

A method is disclosed for using cognitive graph vectors to refinecognitive insights.

More specifically, in one embodiment, the invention relates to a methodfor refining cognitive insights using cognitive graph vectorscomprising: storing data from a plurality of data sources within acognitive graph; associating a first set of the data within thecognitive graph with a first cognitive graph vector of a plurality ofcognitive graph vectors; associating a second set of the data within thecognitive graph with a second cognitive graph vector of the plurality ofcognitive graph vectors; processing the data from the plurality of datasources to provide cognitive insights; and refining the cognitiveinsights based upon a limitation relating to one of the plurality ofcognitive graph vectors.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS);

FIG. 3 is a simplified process flow diagram of a cognitive insightgeneration operations;

FIG. 4 shows a cognitive cloud defined by a plurality of cognitive cloud(CG) vectors;

FIG. 5 shows a first portion of a cognitive cloud defined by a first setof CG vector parameters; and

FIG. 6 shows a second portion of a cognitive cloud defined by a secondset of CG vector parameters.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for usingcognitive graph vectors to refine cognitive insights. The presentinvention may be a system, a method, and/or a computer program product.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 is a generalized illustration of an information processing system100 that can be used to implement the system and method of the presentinvention. The information processing system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, and associated controllers,a hard drive or disk storage 106, and various other subsystems 108. Invarious embodiments, the information processing system 100 also includesnetwork port 110 operable to connect to a network 140, which is likewiseaccessible by a service provider server 142. The information processingsystem 100 likewise includes system memory 112, which is interconnectedto the foregoing via one or more buses 114. System memory 112 furthercomprises operating system (OS) 116 and in various embodiments may alsocomprise cognitive inference and learning system (CILS) 118. In theseand other embodiments, the CILS 118 may likewise comprise inventionmodules 120. In one embodiment, the information processing system 100 isable to download the CILS 118 from the service provider server 142. Inanother embodiment, the CILS 118 is provided as a service from theservice provider server 142.

In various embodiments, the CILS 118 is implemented to perform variouscognitive computing operations described in greater detail herein. Asused herein, cognitive computing broadly refers to a class of computinginvolving self-learning systems that use techniques such as spatialnavigation, machine vision, and pattern recognition to increasinglymimic the way the human brain works. To be more specific, earlierapproaches to computing typically solved problems by executing a set ofinstructions codified within software. In contrast, cognitive computingapproaches are data-driven, sense-making, insight-extracting,problem-solving systems that have more in common with the structure ofthe human brain than with the architecture of contemporary,instruction-driven computers.

To further differentiate these distinctions, traditional computers mustfirst be programmed by humans to perform specific tasks, while cognitivesystems learn from their interactions with data and humans alike, and ina sense, program themselves to perform new tasks. To summarize thedifference between the two, traditional computers are designed tocalculate rapidly. Cognitive systems are designed to quickly drawinferences from data and gain new knowledge.

Cognitive systems achieve these abilities by combining various aspectsof artificial intelligence, natural language processing, dynamiclearning, and hypothesis generation to render vast quantities ofintelligible data to assist humans in making better decisions. As such,cognitive systems can be characterized as having the ability to interactnaturally with people to extend what either humans, or machines, coulddo on their own. Furthermore, they are typically able to process naturallanguage, multi-structured data, and experience much in the same way ashumans. Moreover, they are also typically able to learn a knowledgedomain based upon the best available data and get better, and moreimmersive, over time.

It will be appreciated that more data is currently being produced everyday than was recently produced by human beings from the beginning ofrecorded time. Deep within this ever-growing mass of data is a class ofdata known as “dark data,” which includes neglected information, ambientsignals, and insights that can assist organizations and individuals inaugmenting their intelligence and deliver actionable insights throughthe implementation of cognitive applications. As used herein, cognitiveapplications, or “cognitive apps,” broadly refer to cloud-based, bigdata interpretive applications that learn from user engagement and datainteractions. Such cognitive applications extract patterns and insightsfrom dark data sources that are currently almost completely opaque.Examples of such dark data include disease insights from population-widehealthcare records and social media feeds, or new sources ofinformation, such as sensors monitoring pollution in delicate marineenvironments.

Over time, it is anticipated that cognitive applications willfundamentally change the ways in which many organizations operate asthey invert current issues associated with data volume and variety toenable a smart, interactive data supply chain. Ultimately, cognitiveapplications hold the promise of receiving a user query and immediatelyproviding a data-driven answer from a masked data supply chain inresponse. As they evolve, it is likewise anticipated that cognitiveapplications may enable a new class of “sixth sense” applications thatintelligently detect and learn from relevant data and events to offerinsights, predictions and advice rather than wait for commands. Just asweb and mobile applications changed the way people access data,cognitive applications may change the way people listen to, and becomeempowered by, multi-structured data such as emails, social media feeds,doctors notes, transaction records, and call logs.

However, the evolution of such cognitive applications has associatedchallenges, such as how to detect events, ideas, images, and othercontent that may be of interest. For example, assuming that the role andpreferences of a given user are known, how is the most relevantinformation discovered, prioritized, and summarized from large streamsof multi-structured data such as news feeds, blogs, social media,structured data, and various knowledge bases? To further the example,what can a healthcare executive be told about their competitor's marketshare? Other challenges include the creation of acontextually-appropriate visual summary of responses to questions orqueries.

FIG. 2 is a simplified block diagram of a cognitive inference andlearning system (CILS) implemented in accordance with an embodiment ofthe invention. In various embodiments, the CILS 118 is implemented toincorporate a variety of processes, including semantic analysis 202,goal optimization 204, collaborative filtering 206, common sensereasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 to generatecognitive insights.

As used herein, semantic analysis 202 broadly refers to performingvarious analysis operations to achieve a semantic level of understandingabout language by relating syntactic structures. In various embodiments,various syntactic structures are related from the levels of phrases,clauses, sentences and paragraphs, to the level of the body of contentas a whole and to its language-independent meaning In certainembodiments, the semantic analysis 202 process includes processing atarget sentence to parse it into its individual parts of speech, tagsentence elements that are related to predetermined items of interest,identify dependencies between individual words, and perform co-referenceresolution. For example, if a sentence states that the author reallylikes the hamburgers served by a particular restaurant, then the name ofthe “particular restaurant” is co-referenced to “hamburgers.”

As likewise used herein, goal optimization 204 broadly refers toperforming multi-criteria decision making operations to achieve a givengoal or target objective. In various embodiments, one or more goaloptimization 204 processes are implemented by the CILS 118 to definepredetermined goals, which in turn contribute to the generation of acognitive insight. For example, goals for planning a vacation trip mayinclude low cost (e.g., transportation and accommodations), location(e.g., by the beach), and speed (e.g., short travel time). In thisexample, it will be appreciated that certain goals may be in conflictwith another. As a result, a cognitive insight provided by the CILS 118to a traveler may indicate that hotel accommodations by a beach may costmore than they care to spend.

Collaborative filtering 206, as used herein, broadly refers to theprocess of filtering for information or patterns through thecollaborative involvement of multiple agents, viewpoints, data sources,and so forth. The application of such collaborative filtering 206processes typically involves very large and different kinds of datasets, including sensing and monitoring data, financial data, and userdata of various kinds Collaborative filtering 206 may also refer to theprocess of making automatic predictions associated with predeterminedinterests of a user by collecting preferences or other information frommany users. For example, if person ‘A’ has the same opinion as a person‘B’ for a given issue ‘x’, then an assertion can be made that person ‘A’is more likely to have the same opinion as person ‘B’ opinion on adifferent issue ‘y’ than to have the same opinion on issue ‘y’ as arandomly chosen person. In various embodiments, the collaborativefiltering 206 process is implemented with various recommendation enginesfamiliar to those of skill in the art to make recommendations.

As used herein, common sense reasoning 208 broadly refers to simulatingthe human ability to make deductions from common facts they inherentlyknow. Such deductions may be made from inherent knowledge about thephysical properties, purpose, intentions and possible behavior ofordinary things, such as people, animals, objects, devices, and so on.In various embodiments, common sense reasoning 208 processes areimplemented to assist the CILS 118 in understanding and disambiguatingwords within a predetermined context. In certain embodiments, the commonsense reasoning 208 processes are implemented to allow the CILS 118 togenerate text or phrases related to a target word or phrase to performdeeper searches for the same terms. It will be appreciated that if thecontext of a word is better understood, then a common senseunderstanding of the word can then be used to assist in finding betteror more accurate information. In certain embodiments, this better ormore accurate understanding of the context of a word, and its relatedinformation, allows the CILS 118 to make more accurate deductions, whichare in turn used to generate cognitive insights.

As likewise used herein, natural language processing (NLP) 210 broadlyrefers to interactions with a system, such as the CILS 118, through theuse of human, or natural, languages. In various embodiments, various NLP210 processes are implemented by the CILS 118 to achieve naturallanguage understanding, which enables it to not only derive meaning fromhuman or natural language input, but to also generate natural languageoutput.

Summarization 212, as used herein, broadly refers to processing a set ofinformation, organizing and ranking it, and then generating acorresponding summary. As an example, a news article may be processed toidentify its primary topic and associated observations, which are thenextracted, ranked, and then presented to the user. As another example,summarization operations may be performed on the same news article toidentify individual sentences, rank them, order them, and determinewhich of the sentences are most impactful in describing the article andits content. As yet another example, a structured data record, such as apatient's electronic medical record (EMR), may be processed using thesummarization 212 process to generate sentences and phrases thatdescribes the content of the EMR. In various embodiments, varioussummarization 212 processes are implemented by the CILS 118 to generatesummarizations of content streams, which are in turn used to generatecognitive insights.

As used herein, temporal/spatial reasoning 214 broadly refers toreasoning based upon qualitative abstractions of temporal and spatialaspects of common sense knowledge, derived from common sense reasoningprocesses described in greater detail herein. For example, it is notuncommon for a predetermined set of data to change due to an associatedchange in time or location. Likewise, other attributes, such as itsassociated metadata, may likewise change due to an associated change intime or location. As a result, these changes may affect the context ofthe data. To further the example, the context of asking someone whatthey believe they should be doing at 3:00 in the afternoon during theworkday while they are in their office at work may be quite differentthan asking the same user the same question at 3:00 on a Sundayafternoon when they are relaxing at home in their living room. Invarious embodiments, various temporal/spatial reasoning 214 processesare implemented by the CILS 118 to determine the context of queries, andassociated data, which are in turn used to generate cognitive insights.

As likewise used herein, entity resolution 216 broadly refers to theprocess of finding elements in a set of data that refer to the sameentity across different data sources (e.g., structured, non-structured,streams, devices, etc.), where the target entity does not share a commonidentifier. In various embodiments, the entity resolution 216 process isimplemented by the CILS 118 to identify significant nouns, adjectives,phrases or sentence elements that represent various predeterminedentities within one or more domains. From the foregoing, it will beappreciated that the implementation of one or more of the semanticanalysis 202, goal optimization 204, collaborative filtering 206, commonsense reasoning 208, natural language processing 210, summarization 212,temporal/spatial reasoning 214, and entity resolution 216 processes bythe CILS 118 can facilitate the generation of a semantic, cognitivemodel.

In various embodiments, the CILS 118 receives ambient signals 220,curated data 222, and learned knowledge, which is then processed by theCILS 118 to generate one or more cognitive graphs 226. In turn, the oneor more cognitive graphs 226 are further used by the CILS 118 togenerate cognitive insight streams, which are then delivered to one ormore destinations 230, as described in greater detail herein. In variousembodiments, the destination may include a cognitive application,likewise described in greater detail herein.

As used herein, ambient signals 220 broadly refer to input signals, orother data streams, that may contain data providing additional insightor context to the curated data 222 and learned knowledge 224 received bythe CILS 118. For example, ambient signals may allow the CILS 118 tounderstand that a user is currently using their mobile device, atlocation ‘x’, at time ‘y’, doing activity ‘z’. To further the example,there is a difference between the user using their mobile device whilethey are on an airplane versus using their mobile device after landingat an airport and walking between one terminal and another. To extendthe example even further, ambient signals may add additional context,such as the user is in the middle of a three leg trip and has two hoursbefore their next flight. Further, they may be in terminal Al, but theirnext flight is out of C1, it is lunchtime, and they want to know thebest place to eat. Given the available time the user has, their currentlocation, restaurants that are proximate to their predicted route, andother factors such as food preferences, the CILS 118 can perform variouscognitive operations and provide a recommendation for where the user caneat.

In various embodiments, the curated data 222 may include structured,unstructured, social, public, private, streaming, device or other typesof data described in greater detail herein. In certain embodiments, thelearned knowledge 224 is based upon past observations and feedback fromthe presentation of prior cognitive insight streams and recommendations.In various embodiments, the learned knowledge 224 is provided via afeedback look that provides the learned knowledge 224 in the form of alearning stream of data.

As likewise used herein, a cognitive graph 226 refers to arepresentation of expert knowledge, associated with individuals andgroups over a period of time, to depict relationships between people,places, and things using words, ideas, audio and images. As such, it isa machine-readable formalism for knowledge representation that providesa common framework allowing data and knowledge to be shared and reusedacross user, application, organization, and community boundaries.

In various embodiments, the information contained in, and referenced by,a cognitive graph 226 is derived from many sources (e.g., public,private, social, device), such as curated data 222. In certain instancesof these embodiments, the cognitive graph 226 assists in theidentification and organization of information associated with howpeople, places and things are related to one other. In variousembodiments, the cognitive graph 226 enables automated cognitive agents,described in greater detail herein, to access the Web moreintelligently, enumerate inferences through utilization of curated,structured data 222, and provide answers to questions by serving as acomputational knowledge engine.

In certain embodiments, the cognitive graph 226 not only elicits andmaps expert knowledge by deriving associations from data, it alsorenders higher level insights and accounts for knowledge creationthrough collaborative knowledge modeling. In various embodiments, thecognitive graph 226 is a machine-readable, declarative memory systemthat stores and learns both episodic memory (e.g., specific personalexperiences associated with an individual or entity), and semanticmemory, which stores factual information (e.g., geo location of anairport or restaurant).

For example, the cognitive graph 226 may know that a given airport is aplace, and that there is a list of related places such as hotels,restaurants and departure gates. Furthermore, the cognitive graph 226may know that people such as business travelers, families and collegestudents use the airport to board flights from various carriers, eat atvarious restaurants, or shop at certain retail stores. The cognitivegraph 226 may also have knowledge about the key attributes from variousretail rating sites that travelers have used to describe the food andtheir experience at various venues in the airport over the past sixmonths.

In certain embodiments, the cognitive insight stream 228 isbidirectional, and supports flows of information both to and fromdestinations 230. In these embodiments, the first flow is generated inresponse to receiving a query, and subsequently delivered to one or moredestinations 230. The second flow is generated in response to detectinginformation about a user of one or more of the destinations 230. Suchuse results in the provision of information to the CILS 118. Inresponse, the CILS 118 processes that information, in the context ofwhat it knows about the user, and provides additional information to theuser, such as a recommendation. In various embodiments, the cognitiveinsight stream 228 is configured to be provided in a “push” streamconfiguration familiar to those of skill in the art. In certainembodiments, the cognitive insight stream 228 is implemented to usenatural language approaches familiar to skilled practitioners of the artto support interactions with a user. In various embodiments, thedestinations 230 include a cognitive application used by such a user.

In various embodiments, the cognitive insight stream 228 may include astream of visualized insights. As used herein, visualized insightsbroadly refer to cognitive insights that are presented in a visualmanner, such as a map, an infographic, images, and so forth. In certainembodiments, these visualized insights may include various cognitiveinsights, such as “What happened?”, “What do I know about it?”, “What islikely to happen next?”, or “What should I do about it?” In theseembodiments, the cognitive insight stream is generated by variouscognitive agents, which are applied to various sources, datasets, andcognitive graphs. As used herein, a cognitive agent broadly refers to acomputer program that performs a task with minimum specific directionsfrom users and learns from each interaction with data and human users.

In various embodiments, the CILS 118 delivers Cognition as a Service(CaaS). As such, it provides a cloud-based development and executionplatform that allow various cognitive applications and services tofunction more intelligently and intuitively. In certain embodiments,cognitive applications powered by the CILS 118 are able to think andinteract with users as intelligent virtual assistants. As a result,users are able to interact with such cognitive applications by askingthem questions and giving them commands. In response, these cognitiveapplications will be able to assist the user in completing tasks andmanaging their work more efficiently.

In these and other embodiments, the CILS 118 can operate as an analyticsplatform to process big data, and dark data as well, to provide dataanalytics through a public, private or hybrid cloud environment. As usedherein, cloud analytics broadly refers to a service model wherein datasources, data models, processing applications, computing power, analyticmodels, and sharing or storage of results are implemented within a cloudenvironment to perform one or more aspects of analytics.

In various embodiments, users submit queries and computation requests ina natural language format to the CILS 118. In response, they areprovided with a ranked list of relevant answers and aggregatedinformation with useful links and pertinent visualizations through agraphical representation. In these embodiments, the cognitive graph 226generates semantic and temporal maps to reflect the organization ofunstructured data and to facilitate meaningful learning from potentiallymillions of lines of text, much in the same way as arbitrary syllablesstrung together create meaning through the concept of language.

FIG. 3 is a simplified process flow diagram of a cognitive insightgeneration operations performed in accordance with an embodiment of theinvention. In various embodiments, cognitive insight operations may beperformed in various phases. In this embodiment, these phases include adata lifecycle 340 phase, a learning 338 phase, and anapplication/insight composition 340 phase.

In the data lifecycle 336 phase, a predetermined cognitive platform 310instantiation sources social data 312, public data 314, licensed data316, and proprietary data 318 from various sources. For example, theproprietary data 318 may include privately-owned data, such as anairline's frequent flier information that is only used internally to theairline. In various embodiments, the cognitive platform 310instantiation includes a cognitive inference and learning system (CILS),such as the CILS 118 shown in FIGS. 1 and 2. In these and otherembodiments, the cognitive platform 310 instantiation includes a source306 component, a process 308 component, a deliver 310 component, acleanse 320 component, an enrich 322 component, a filter/transform 324component, and a repair/reject 326 component. Likewise, as shown in FIG.3, the process 308 component includes a repository of models 328. Asused herein, models 328 broadly refer to machine learning models. Incertain embodiments, the models include one or more statistical models.

In various embodiments, the process 308 component is implemented toperform various cognitive insight generation and other processingoperations. In these embodiments, the process 308 component isimplemented to interact with the source 306 component, which in turn isimplemented to perform various data sourcing operations familiar tothose of skill in the art. In various embodiments, the sourcingoperations are performed by one or more sourcing agents. In theseembodiments, the sourcing agents are implemented to source a variety ofmulti-site, multi-structured source streams of data. In certainembodiments, the sourcing agents may include a batch upload agent, anAPI connectors agent, a real-time streams agent, a Structured QueryLanguage (SQL)/Not Only SQL (NoSQL) databases agent, a message enginesagent, and one or more custom sourcing agents. Skilled practitioners ofthe art will realize that other types of sourcing agents may be used invarious embodiments and the foregoing is not intended to limit thespirit, scope or intent of the invention.

The resulting sourced data is then provided to the process 308component. In turn, the process 308 component is implemented to interactwith the cleanse 320 component, which in turn is implemented to performvarious data cleansing operations familiar to those of skill in the art.As an example, the cleanse 320 component may perform data normalizationor pruning operations, likewise known to skilled practitioners of theart. In certain embodiments, the cleanse 320 component may beimplemented to interact with the repair/reject 326 component, which inturn is implemented to perform various data repair or data rejectionoperations known to those of skill in the art.

Once data cleansing, repair and rejection operations are completed, theprocess 308 component is implemented to interact with the enrich 322component, which is implemented to perform various data enrichmentoperations familiar to those of skill in the art. As an example, a datastream may be sourced from Associated Press® by a sourcing agent. Theenrich 322 component may then enrich the data stream by performingsentiment analysis, geotagging, and entity detection operations togenerate an enriched data stream. In certain embodiments, the enrichmentoperations may include filtering operations familiar to skilledpractitioners of the art. To further the preceding example, theAssociated Press ® data stream may be filtered by a predeterminedgeography attribute to generate an enriched data stream.

Once data enrichment operations have been completed, the process 308component is likewise implemented to interact with the filter/transform324, which in turn is implemented to perform data filtering andtransformation operations familiar to those of skill in the art. Invarious embodiments, the process 308 component is implemented togenerate various models, described in greater detail herein, which arestored in the repository of models 328. The process 308 component islikewise implemented in various embodiments use the sourced data togenerate one or more cognitive graphs 226, as described in greaterdetail herein. In various embodiments, the process 308 component isimplemented to gain an understanding of the data sourced from thesources of social data 312, public data 314, licensed data 316, andproprietary data 318, which assist in the automated generation of thecognitive graph 226.

The process 308 component is likewise implemented in various embodimentsto perform bridging 346 operations. In these and other embodiments, thebridging 346 operations may be performed to provide domain-specificresponses when bridging a translated query to a cognitive graph. Forexample, the same query bridged to a target cognitive graph 226 mayresult in different answers for different domains, dependent upondomain-specific bridging operations performed to access the cognitivegraph 226.

In certain embodiments, the bridging 346 operations are performed bybridging agents. In these embodiments, the bridging agent interprets atranslated query generated by the user query 342 within a predetermineduser context, and then maps it to predetermined nodes and links within atarget cognitive graph 226. In various embodiments, the cognitive graph226 is accessed by the process 308 component during the learning 336phase of the cognitive insight generation operations.

In various embodiments, a cognitive application 304 is implemented toreceive user input, such as a user query 342, which is then submittedduring the application/insight composition 840 phase to a graph queryengine 326. In turn, the graph query engine 326 processes the user query342 to generate a graph query 344. The graph query 344 is then used toquery the cognitive graph 226, which results in the generation of one ormore cognitive insights. In various embodiments, the process 308component is implemented to provide these cognitive insights to thedeliver 310, which in turn is implemented to deliver the cognitiveinsights in the form of a visual data summary 348 to the cognitiveapplication 304. In various embodiments, learning operations areiteratively performed during the learning 338 phase to provide moreaccurate and useful cognitive insights.

FIG. 4 shows a cognitive cloud defined by a plurality of cognitive graph(CG) vectors implemented in accordance with an embodiment of theinvention. In this embodiment, the cumulative data domain represented bya cognitive graph 226 is defined by a plurality of CG vectors V₁ 404, V₂406, V₃ 408, V₄ 410, V₅ 412 and V₆ 414, each of which extends from a CGnexus 438. In various embodiments, each of the CG vectors V₁ 404, V₂406, V₃ 408, V₄ 410, V₅ 412 and V₆ 414 is associated with apredetermined set of data within the cognitive graph 226. In these andother embodiments, the cognitive graph 226 may be defined by additionalCG vectors than those shown in FIG. 4. In certain embodiments, thecognitive graph 226 may be defined by fewer CG vectors than those shownin FIG. 4.

As shown in FIG. 4, each of the CG vectors V₁ 404, V₂ 406, V₃ 408, V₄410, V₅ 412 and V₆ 414 includes a plurality of CG vector indices 436corresponding to predetermined attributes associated with the datadomain represented by the cognitive graph 226. In various embodiments,the magnitude of the CG vector indices 436 is substantially similar. Incertain embodiments, the magnitude of the CG vector indices 436 isdifferent. In various embodiments, CG vector parameters V₁P 424, V₂P426, V₃P 428, V₄P 430, V₅P 432 and V₆P 434 are selected, whichcorrespond to their respective CG vector indices 436. In theseembodiments, the selected CG vector parameters V₁P 424, V₂P 426, V₃P428, V₄P 430, V₅P 432 and V₆P 434 likewise correspond to user inputvalues, which are used as described in greater detail herein to generatea cognitive insight.

In various embodiments, the selected CG vector parameters V₁P 424, V₂P426, V₃P 428, V₄P 430, V₅P 432 and V₆P 434 define a portion 440 of thecognitive graph 226 used to generate a cognitive insight. As an example,as shown in FIG. 4, the selected CG vector parameters V₁P 424, V₂P 426,V₃P 428, V₄P 430, V₅P 432 and V₆P 434 are at the depicted extremities oftheir respective CG vectors V₁ 404, V₂ 406, V₃ 408, V₄ 410, V₅ 412 andV₆ 414. As such, a substantive portion 440 of the cognitive graph 226 isused to generate the cognitive insight.

FIG. 5 shows a first portion of a cognitive cloud defined by a first setof cognitive graph (CG) vector parameters implemented in accordance withan embodiment of the invention. In this embodiment, a first set of CGvector parameters V₁P 524, V₂P 526, V₃P 528, V₄P 530, V₅P 532 and V₆P534, corresponding to a first set of user input values, are used todefine a first portion 540 of a cognitive graph 226 used to generate afirst cognitive insight.

FIG. 6 shows a second portion of a cognitive cloud defined by a secondset of cognitive graph (CG) vector parameters implemented in accordancewith an embodiment of the invention. In this embodiment, a second set ofCG vector parameters V₁P 624, V₂P 626, V₃P 628, V₄P 630, V₅P 632 and V₆P634, corresponding to a second set of user input values, are used todefine a second portion 640 of a cognitive graph 226 used to generate asecond cognitive insight. In various embodiments, the second set of userinput values are based upon the first set of user input values describedin the descriptive text associated with FIG. 5. In these embodiments,the first set of CG vector parameters V₁P 524, V₂P 526, V₃P 528, V₄P530, V₅P 532 and V₆P 534 are modified to generate the second set of CGvector parameters V₁P 624, V₂P 626, V₃P 628, V₄P 630, V₅P 632 and V₆P634. In various embodiments, additional such modifications areiteratively performed on the second set of CG vector parameters V₁P 624,V₂P 626, V₃P 628, V₄P 630, V₅P 632 and V₆P 634 to define additionalportions 640 of the cognitive graph 226, which in turn are used togenerate additional cognitive insights.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A method for refining cognitive insights using cognitive graph vectors comprising: storing data from a plurality of data sources within a cognitive graph; associating a first set of the data within the cognitive graph with a first cognitive graph vector of a plurality of cognitive graph vectors; associating a second set of the data within the cognitive graph with a second cognitive graph vector of the plurality of cognitive graph vectors; processing the data from the plurality of data sources to provide cognitive insights; and refining the cognitive insights based upon a limitation relating to one of the plurality of cognitive graph vectors.
 2. The method of claim 1, wherein: the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus; the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph vector; the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices; and the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices.
 3. The method of claim 1, further comprising: associating a third set of data within the cognitive graph with a third cognitive graph vector of the plurality of cognitive graph vectors; and, wherein the refining the cognitive insights based upon the limitation relating to one of the plurality of cognitive graph vectors further comprises identifying a limitation on one of the first, second and third cognitive graph vectors and refining another of the first, second and third cognitive graph vectors based upon the limitation of one of the first, second and third cognitive graph vectors.
 4. The method of claim 3, wherein: the first cognitive graph vector comprises a plurality of first cognitive graph vector indices extending along the first cognitive graph vector away from a cognitive graph nexus; the second cognitive graph vector comprises a plurality of second cognitive graph vector indices extending along the second cognitive graph vector away from the cognitive graph vector; the third cognitive graph vector comprises a plurality of third cognitive graph vector indices extending along the third cognitive graph vector away from the cognitive graph vector; the limitation comprises limiting the first set of data to data within a first certain index of the plurality of first cognitive graph vector indices; the refining comprising limiting the second set of data to data within a second certain index of the second cognitive graph vector indices and data within a third; and the refining further comprising limiting the third set of data to data within a third certain index of the third cognitive graph vector indices.
 5. The method of claim 4, wherein: at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices are different magnitudes.
 6. The method of claim 4, wherein: at least some of the first cognitive graph vector indices, second cognitive graph vector indices and third vector graph indices are substantially similar magnitudes. 